Conference Agenda (All times are shown in Eastern Daylight Time)

This paper explores the phenomenon of news deserts as information problems to navigate research opportunities and theorize its dynamics. Drawing on the theory of local information landscapes, news deserts are conceptualized as more than merely an absence of news organizations or content; rather, emphasizing the structural and material dimensions of local news ecosystems, such as fragmentation, transience, and inconsistent distribution. We argue that news deserts should be understood as material pre-conditions of people’s inability to access, interpret, and engage with local information. To empirically ground this conceptualization, we conduct a case study in Alabama, using over 30,000 news articles from a local news operation. Through geographic, thematic, and temporal analyses, paired with population and crime statistics, we uncover patterns of underreporting, geographic bias, and thematic concentration. Our findings demonstrate that news deserts can emerge even in areas served by active news outlets, contributing to a broader understanding of the uneven distribution of local information, vital for civic engagement and community well-being.

AI and data are mutually influential, with AI outputs shaped by training data and data often generated, processed, and categorized by AI. The use of both AI and data by government organizations is guided by policy documents; existing research has explored data policies or AI policies but has rarely put both in conversation, despite their linked subject matter. We adopt a mixed-methods approach to analyze the data and AI strategies of the Government of Canada, investigating whether the data-AI relationship is reflected in policy documents. Our findings demonstrate a disconnect between Canadian data and AI policies, illustrate potential implications of this disconnect, and contribute to ASIS&T 2025 conversations about the necessity of information science for the responsible, ethical use of data and AI in government settings.

Increasing political polarization poses a significant threat to democratic functioning, hindering constructive dialogue and collaborative problem-solving. This paper explores the potential of information science, specifically through the application of sensemaking tools employing Artificial Intelligence (AI), to address this challenge. Drawing on theories of sensemaking and framing, we examine how technology can support structured dialogue initiatives by using sensemaking tools to support the political depolarization work of the non-profit organization Braver Angels. We introduce two specific sensemaking tools – Issue Sensemaking and Article Sensemaking – designed to help individuals explore complex political issues from multiple perspectives, analyze information sources critically, and identify areas of common ground and disagreement. By facilitating deeper understanding and more structured engagement with diverse viewpoints and information artifacts, these tools offer a promising avenue for improving the quality of civic discourse and potentially reducing affective polarization. This work aligns with the need for information science to contribute responsible, human-centered solutions in turbulent socio-political contexts.

The study examines the datafication process in China's elderly care service policies through the lenses of the data value chain and data gaze frameworks. As China faces rapid population aging, digital transformation has become a strategic priority to enhance care quality and efficiency. The research analyzes 103 national policies (2011–2024) to map how data value is created across four stages: collection, organization, circulation, and utilization. Findings reveal an uneven policy focus, with heavy emphasis on foundational infrastructure (data collection and organization) but limited attention to data utilization and ethical governance. The data gaze analysis highlights a top-down vision prioritizing technical systems and regulatory control, often overlooking user inclusivity and practical implementation challenges. While policies have established platforms for data integration and sharing, gaps persist in capacity building, stakeholder collaboration, and equitable service delivery. The study contributes theoretically by integrating the data value chain and data gaze to critique policy frameworks, and practically by identifying actionable areas for improvement, such as fostering inclusive participation and strengthening late-stage data value creation. The study offers valuable lessons for global research on public sector data governance.